Various personal assistant software applications have been developed that respond to a user's text or natural language request by carrying out corresponding tasks. For example, a personal assistant application accessed by a smartphone user may make look up phone numbers and place calls, search for restaurants, draft and send emails, make calendar entries, and so on. In that regard, a particular user will often phrase a request for a given task differently from other users' phrasings. Such variation is inherent with regard to human interaction. Due to this variability, conventional personal assistant software applications are typically error-prone with regard to properly executing the desired request of a user. Moreover, the resulting applications tend to be ad hoc and thus difficult to apply to different languages.
Accordingly, there is a need in the art for language-independent improved systems having better accuracy with regard to classifying and executing tasks wanted by users.